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Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181250/ https://www.ncbi.nlm.nih.gov/pubmed/35683417 http://dx.doi.org/10.3390/jcm11113030 |
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author | Yamashita, Taro Kurumi, Hiroki Fujii, Masashi Sakaguchi, Takuki Hashimoto, Takeshi Kinoshita, Hidehito Kanda, Tsutomu Onoyama, Takumi Ikebuchi, Yuichiro Yoshida, Akira Kawaguchi, Koichiro Yashima, Kazuo Isomoto, Hajime |
author_facet | Yamashita, Taro Kurumi, Hiroki Fujii, Masashi Sakaguchi, Takuki Hashimoto, Takeshi Kinoshita, Hidehito Kanda, Tsutomu Onoyama, Takumi Ikebuchi, Yuichiro Yoshida, Akira Kawaguchi, Koichiro Yashima, Kazuo Isomoto, Hajime |
author_sort | Yamashita, Taro |
collection | PubMed |
description | Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to identify gastric tumors from PDD images. Method one was applied to segmented regions in the PDD endoscopic image to determine the region in LAB color space to be attributed to tumors using a multi-layer neural network. Method two aimed to diagnose tumors and determine regions in the PDD endoscopic image attributed to tumors using the convoluted neural network method. The efficiencies of diagnosing tumors were 77.8% (7/9) and 93.3% (14/15) for method one and method two, respectively. The efficiencies of determining tumor region defined as the ratio of the area were 35.7% (0.0–78.0) and 48.5% (3.0–89.1) for method one and method two, respectively. False-positive rates defined as the ratio of the area were 0.3% (0.0–2.0) and 3.8% (0.0–17.4) for method one and method two, respectively. Objective methods of determining tumor region in 5-aminolevulinic acid-based endoscopic PDD were developed by identifying regions in LAB color space attributed to tumors or by applying a method of convoluted neural network. |
format | Online Article Text |
id | pubmed-9181250 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91812502022-06-10 Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors Yamashita, Taro Kurumi, Hiroki Fujii, Masashi Sakaguchi, Takuki Hashimoto, Takeshi Kinoshita, Hidehito Kanda, Tsutomu Onoyama, Takumi Ikebuchi, Yuichiro Yoshida, Akira Kawaguchi, Koichiro Yashima, Kazuo Isomoto, Hajime J Clin Med Article Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to identify gastric tumors from PDD images. Method one was applied to segmented regions in the PDD endoscopic image to determine the region in LAB color space to be attributed to tumors using a multi-layer neural network. Method two aimed to diagnose tumors and determine regions in the PDD endoscopic image attributed to tumors using the convoluted neural network method. The efficiencies of diagnosing tumors were 77.8% (7/9) and 93.3% (14/15) for method one and method two, respectively. The efficiencies of determining tumor region defined as the ratio of the area were 35.7% (0.0–78.0) and 48.5% (3.0–89.1) for method one and method two, respectively. False-positive rates defined as the ratio of the area were 0.3% (0.0–2.0) and 3.8% (0.0–17.4) for method one and method two, respectively. Objective methods of determining tumor region in 5-aminolevulinic acid-based endoscopic PDD were developed by identifying regions in LAB color space attributed to tumors or by applying a method of convoluted neural network. MDPI 2022-05-27 /pmc/articles/PMC9181250/ /pubmed/35683417 http://dx.doi.org/10.3390/jcm11113030 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yamashita, Taro Kurumi, Hiroki Fujii, Masashi Sakaguchi, Takuki Hashimoto, Takeshi Kinoshita, Hidehito Kanda, Tsutomu Onoyama, Takumi Ikebuchi, Yuichiro Yoshida, Akira Kawaguchi, Koichiro Yashima, Kazuo Isomoto, Hajime Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
title | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
title_full | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
title_fullStr | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
title_full_unstemmed | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
title_short | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
title_sort | objective methods of 5-aminolevulinic acid-based endoscopic photodynamic diagnosis using artificial intelligence for identification of gastric tumors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181250/ https://www.ncbi.nlm.nih.gov/pubmed/35683417 http://dx.doi.org/10.3390/jcm11113030 |
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